title: Gene expression rule discovery and multi-objective ROC analysis using a neural-genetic hybrid.
abstract: Microarray data allows an unprecedented view of the biochemical mechanisms contained within a cell although deriving useful information from the data is still proving to be a difficult task. In this paper, a novel method based on a multi-objective genetic algorithm is investigated that evolves a near-optimal trade-off between Artificial Neural Network (ANN) classifier accuracy (sensitivity and specificity) and size (number of genes). This hybrid method is shown to work on four well-established gene expression data sets taken from the literature. The results provide evidence for the rule discovery ability of the hybrid method and indicate that the approach can return biologically intelligible as well as plausible results and requires no pre-filtering or pre-selection of genes.
authors.affiliation: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, UK. E.C.Keedwell@ex.ac.uk
authors.forename: Ed
authors.initials: E
authors.lastname: Keedwell
authors.forename: Ajit
authors.initials: A
authors.lastname: Narayanan
mesh_terms: D000465:Algorithms; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics; D008223:Lymphoma / Q000235:genetics / Q000378:metabolism; D009101:Multiple Myeloma / Q000235:genetics / Q000378:metabolism; D016571:Neural Networks, Computer; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics; D012372:ROC Curve